Today we will…
map() familyYou must complete the objectives and write up the written components outlined under Section 1 on the Project Details page on Canvas.
Type out the task over and over.
Do not do this.
Repeatedly execute the same operation over and over.
for() and while()) allow us to iterate.Many operations in R are vectorized.
Not every function is (or can be) vectorized.
if() statements cannot operate on vectors.The if(x > 0) statement can only be checked for something of length 1 (a single number, not a vector).
Not every function is (or can be) vectorized.
if() statements cannot operate on vectors.Not every function is (or can be) vectorized.
if() statements cannot operate on vectors.if() statements? case_when()pos_neg_zero <- function(x){
state <- case_when(x > 0 ~ "Greater than 0!",
x < 0 ~ "Less than 0!",
.default = "Equal to 0!")
return(state)
}
my_vec <- seq(from = -4, to = 3)
pos_neg_zero(x = my_vec)[1] "Less than 0!" "Less than 0!" "Less than 0!" "Less than 0!"
[5] "Equal to 0!" "Greater than 0!" "Greater than 0!" "Greater than 0!"
Applying class() to a single variable in a dataframe returns the data type of that column:
Trying to apply class() to every variable in a data.frame returns the data type of the data.frame:
Write a for() loop…
data_type <- rep(NA, length = ncol(penguins))
for(i in 1:ncol(penguins)){
data_type[i] <- class(penguins[[i]])
}
data_type[1] "factor" "factor" "numeric" "numeric" "integer" "integer" "factor"
[8] "integer"
… but loops are computationally intensive!
What about across()?
# A tibble: 1 × 8
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
<chr> <chr> <chr> <chr> <chr> <chr>
1 factor factor numeric numeric integer integer
# ℹ 2 more variables: sex <chr>, year <chr>
Ugh. Internally, across() uses a for() loop!
for (j in seq_fns) {
fn <- fns[[j]]
out[[k]] <- fn(col, ...)
k <- k + 1L
…
To understand computations in R, two slogans are helpful:
Everything that exists is an object.
Everything that happens is a function call.
John Chambers (creator of the pre-cursor to R)
What’s the big picture?
Note
There are a slew of apply() functions you will likely come across.
We will instead focus on the purrr package and the map() family of functions.
purrrThe purrr package breaks common list manipulations into small, independent pieces.
This strategy involves two steps:
|>.purrr will generalize the solution to every element in the list.A list is a 1-dimensional, heterogeneous data structure.
[] or [[]].A dataframe / tibble is a specially formatted list of columns!
# A tibble: 8 × 1
bill_length_mm
<dbl>
1 39.1
2 39.5
3 40.3
4 NA
5 36.7
6 39.3
7 38.9
8 39.2
[1] 39.1 39.5 40.3 NA 36.7 39.3 38.9 39.2
The purrr package works for lists, so it works for data.frames.
map()The map() function iterates through each item in a list and applies a function, then returns the new list.
Note: the first argument in map() is the list, so if we pipe into it, we only specify the function!
map() + DataframesA dataframe is just a list of columns – map() will apply a function to every column.
map() FamilyThe map_xxx() variants allow you to specify the type of output you want.
map() creates a list.map_chr() creates a character vector.map_lgl() creates an logical vector.map_int() creates a integer vector.map_dbl() creates a numeric vector.All take in a list and a function as arguments.
map() + penguinsCalculate the mean of each column.
bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
43.92193 17.15117 200.91520 4201.75439
Output is a vector of doubles.
Calculate the number of NAs in each column.
species island bill_length_mm bill_depth_mm
0 0 2 2
flipper_length_mm body_mass_g sex year
2 2 11 0
Output is a vector of integers.
map_if()The map_if() function allows us to conditionally apply a function to each item in a list.
# A tibble: 6 × 5
species island bill_length_mm[,1] bill_depth_mm[,1] sex
<fct> <fct> <dbl> <dbl> <fct>
1 Adelie Torgersen -0.883 0.784 male
2 Adelie Torgersen -0.810 0.126 female
3 Adelie Torgersen -0.663 0.430 female
4 Adelie Torgersen NA NA <NA>
5 Adelie Torgersen -1.32 1.09 female
6 Adelie Torgersen -0.847 1.75 male
$species
[1] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[8] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[15] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[22] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[29] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[36] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[43] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[50] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[57] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[64] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[71] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[78] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[85] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[92] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[99] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[106] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[113] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[120] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[127] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[134] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[141] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[148] Adelie Adelie Adelie Adelie Adelie Gentoo Gentoo
[155] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[162] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[169] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[176] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[183] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[190] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[197] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[204] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[211] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[218] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[225] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[232] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[239] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[246] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[253] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[260] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[267] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
[274] Gentoo Gentoo Gentoo Chinstrap Chinstrap Chinstrap Chinstrap
[281] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[288] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[295] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[302] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[309] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[316] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[323] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[330] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[337] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[344] Chinstrap
Levels: Adelie Chinstrap Gentoo
$island
[1] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
[8] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
[15] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Biscoe
[22] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[29] Biscoe Biscoe Dream Dream Dream Dream Dream
[36] Dream Dream Dream Dream Dream Dream Dream
[43] Dream Dream Dream Dream Dream Dream Dream
[50] Dream Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[57] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[64] Biscoe Biscoe Biscoe Biscoe Biscoe Torgersen Torgersen
[71] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
[78] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
[85] Dream Dream Dream Dream Dream Dream Dream
[92] Dream Dream Dream Dream Dream Dream Dream
[99] Dream Dream Biscoe Biscoe Biscoe Biscoe Biscoe
[106] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[113] Biscoe Biscoe Biscoe Biscoe Torgersen Torgersen Torgersen
[120] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
[127] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Dream
[134] Dream Dream Dream Dream Dream Dream Dream
[141] Dream Dream Dream Dream Dream Dream Dream
[148] Dream Dream Dream Dream Dream Biscoe Biscoe
[155] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[162] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[169] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[176] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[183] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[190] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[197] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[204] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[211] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[218] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[225] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[232] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[239] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[246] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[253] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[260] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[267] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
[274] Biscoe Biscoe Biscoe Dream Dream Dream Dream
[281] Dream Dream Dream Dream Dream Dream Dream
[288] Dream Dream Dream Dream Dream Dream Dream
[295] Dream Dream Dream Dream Dream Dream Dream
[302] Dream Dream Dream Dream Dream Dream Dream
[309] Dream Dream Dream Dream Dream Dream Dream
[316] Dream Dream Dream Dream Dream Dream Dream
[323] Dream Dream Dream Dream Dream Dream Dream
[330] Dream Dream Dream Dream Dream Dream Dream
[337] Dream Dream Dream Dream Dream Dream Dream
[344] Dream
Levels: Biscoe Dream Torgersen
$bill_length_mm
[,1]
[1,] -0.88320467
[2,] -0.80993901
[3,] -0.66340769
[4,] NA
[5,] -1.32279862
[6,] -0.84657184
[7,] -0.91983750
[8,] -0.86488825
[9,] -1.79902541
[10,] -0.35202864
[11,] -1.12131806
[12,] -1.12131806
[13,] -0.51687637
[14,] -0.97478674
[15,] -1.70744334
[16,] -1.34111504
[17,] -0.95647033
[18,] -0.26044656
[19,] -1.74407616
[20,] 0.38062795
[21,] -1.12131806
[22,] -1.13963448
[23,] -1.46932994
[24,] -1.04805240
[25,] -0.93815391
[26,] -1.57922843
[27,] -0.60845845
[28,] -0.62677486
[29,] -1.10300165
[30,] -0.62677486
[31,] -0.80993901
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[44,] 0.03261607
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[151,] -1.45101353
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[156,] 1.11328455
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[225,] 0.78358908
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[227,] 0.45389361
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[229,] 0.65537418
[230,] 1.31476511
[231,] 0.23409663
[232,] 0.23409663
[233,] 0.94843681
[234,] 1.57119492
[235,] 0.63705776
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[237,] 0.17914739
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[240,] 1.35139794
[241,] 0.65537418
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[243,] 0.65537418
[244,] 1.51624567
[245,] 0.28904588
[246,] 1.02170247
[247,] 0.10588173
[248,] 1.25981586
[249,] 1.00338606
[250,] 0.54547569
[251,] 0.82022191
[252,] 1.31476511
[253,] 0.83853832
[254,] 2.19395302
[255,] 0.60042493
[256,] 0.94843681
[257,] 0.61874135
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[259,] -0.40697788
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[261,] -0.11391525
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[263,] 1.20486662
[264,] 1.07665172
[265,] -0.07728242
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[267,] 0.41726078
[268,] 2.04742170
[269,] 0.10588173
[270,] 0.89348757
[271,] 0.60042493
[272,] NA
[273,] 0.52715927
[274,] 1.18655021
[275,] 0.23409663
[276,] 1.09496813
[277,] 0.47221003
[278,] 1.11328455
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[289,] 0.56379210
[290,] 1.47961284
[291,] 0.36231154
[292,] 1.20486662
[293,] 1.16823379
[294,] 2.57859773
[295,] 0.45389361
[296,] 0.96675323
[297,] -0.27876298
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[300,] 1.22318303
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[331,] -0.26044656
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[335,] 1.14991738
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[338,] 0.52715927
[339,] 0.32567871
[340,] 2.17563660
[341,] -0.07728242
[342,] 1.04001889
[343,] 1.25981586
[344,] 1.14991738
attr(,"scaled:center")
[1] 43.92193
attr(,"scaled:scale")
[1] 5.459584
$bill_depth_mm
[,1]
[1,] 0.78430007
[2,] 0.12600328
[3,] 0.42983257
[4,] NA
[5,] 1.08812936
[6,] 1.74642615
[7,] 0.32855614
[8,] 1.24004400
[9,] 0.48047078
[10,] 1.54387329
[11,] -0.02591137
[12,] 0.07536506
[13,] 0.22727971
[14,] 2.05025544
[15,] 1.99961722
[16,] 0.32855614
[17,] 0.93621471
[18,] 1.79706436
[19,] 0.63238542
[20,] 2.20217008
[21,] 0.58174721
[22,] 0.78430007
[23,] 1.03749114
[24,] 0.48047078
[25,] 0.02472685
[26,] 0.88557650
[27,] 0.73366185
[28,] 0.37919435
[29,] 0.73366185
[30,] 0.88557650
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attr(,"scaled:center")
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attr(,"scaled:scale")
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$sex
[1] male female female <NA> female male female male <NA> <NA>
[11] <NA> <NA> female male male female female male female male
[21] female male female male male female male female female male
[31] female male female male female male male female female male
[41] female male female male female male male <NA> female male
[51] female male female male female male female male female male
[61] female male female male female male female male female male
[71] female male female male female male female male female male
[81] female male female male female male male female male female
[91] female male female male female male female male female male
[101] female male female male female male female male female male
[111] female male female male female male female male female male
[121] female male female male female male female male female male
[131] female male female male female male female male female male
[141] female male female male female male male female female male
[151] female male female male female male male female female male
[161] female male female male female male female male female male
[171] female male male female female male female male <NA> male
[181] female male male female female male female male female male
[191] female male female male female male male female female male
[201] female male female male female male female male female male
[211] female male female male female male female male <NA> male
[221] female male female male male female female male female male
[231] female male female male female male female male female male
[241] female male female male female male female male male female
[251] female male female male female male <NA> male female male
[261] female male female male female male female male <NA> male
[271] female <NA> female male female male female male male female
[281] male female female male female male female male female male
[291] female male male female female male female male female male
[301] female male female male female male female male female male
[311] male female female male female male male female male female
[321] female male female male male female female male female male
[331] female male female male male female male female female male
[341] female male male female
Levels: female male
$year
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[286,] -1.25748435
[287,] -1.25748435
[288,] -1.25748435
[289,] -1.25748435
[290,] -1.25748435
[291,] -1.25748435
[292,] -1.25748435
[293,] -1.25748435
[294,] -1.25748435
[295,] -1.25748435
[296,] -1.25748435
[297,] -1.25748435
[298,] -1.25748435
[299,] -1.25748435
[300,] -1.25748435
[301,] -1.25748435
[302,] -1.25748435
[303,] -0.03552216
[304,] -0.03552216
[305,] -0.03552216
[306,] -0.03552216
[307,] -0.03552216
[308,] -0.03552216
[309,] -0.03552216
[310,] -0.03552216
[311,] -0.03552216
[312,] -0.03552216
[313,] -0.03552216
[314,] -0.03552216
[315,] -0.03552216
[316,] -0.03552216
[317,] -0.03552216
[318,] -0.03552216
[319,] -0.03552216
[320,] -0.03552216
[321,] 1.18644003
[322,] 1.18644003
[323,] 1.18644003
[324,] 1.18644003
[325,] 1.18644003
[326,] 1.18644003
[327,] 1.18644003
[328,] 1.18644003
[329,] 1.18644003
[330,] 1.18644003
[331,] 1.18644003
[332,] 1.18644003
[333,] 1.18644003
[334,] 1.18644003
[335,] 1.18644003
[336,] 1.18644003
[337,] 1.18644003
[338,] 1.18644003
[339,] 1.18644003
[340,] 1.18644003
[341,] 1.18644003
[342,] 1.18644003
[343,] 1.18644003
[344,] 1.18644003
attr(,"scaled:center")
[1] 2008.029
attr(,"scaled:scale")
[1] 0.8183559
# A tibble: 6 × 5
species island bill_length_mm[,1] bill_depth_mm[,1] sex
<fct> <fct> <dbl> <dbl> <fct>
1 Adelie Torgersen -0.883 0.784 male
2 Adelie Torgersen -0.810 0.126 female
3 Adelie Torgersen -0.663 0.430 female
4 Adelie Torgersen NA NA <NA>
5 Adelie Torgersen -1.32 1.09 female
6 Adelie Torgersen -0.847 1.75 male
pmap() + FamilyThese functions take in a list of vectors and a function.
map() FamilyThere are so many functions in the map() family – check out the purrr cheatsheet!
Today we will…
map2() familymap2() FamilyThese functions allow us to iterate over two lists at the same time.
map2() FamilyThese functions include:
map2()map2_chr()map2_lgl()map2_int()map2_dbl()Each function has two list arguments, denoted .x and .y, and a function argument.
map2() ExampleFind the minimum.
map2() ExampleRegress vehicle MPG on vehicle weight. Do the regression separately for 4-, 6-, and 8-cylinder vehicles.
<list_of<
tbl_df<
mpg : double
cyl : double
disp: double
hp : double
drat: double
wt : double
qsec: double
vs : double
am : double
gear: double
carb: double
>
>[3]>
[[1]]
# A tibble: 11 × 11
mpg cyl disp hp drat wt qsec vs am gear carb
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
2 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
3 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
4 32.4 4 78.7 66 4.08 2.2 19.5 1 1 4 1
5 30.4 4 75.7 52 4.93 1.62 18.5 1 1 4 2
6 33.9 4 71.1 65 4.22 1.84 19.9 1 1 4 1
7 21.5 4 120. 97 3.7 2.46 20.0 1 0 3 1
8 27.3 4 79 66 4.08 1.94 18.9 1 1 4 1
9 26 4 120. 91 4.43 2.14 16.7 0 1 5 2
10 30.4 4 95.1 113 3.77 1.51 16.9 1 1 5 2
11 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2
[[2]]
# A tibble: 7 × 11
mpg cyl disp hp drat wt qsec vs am gear carb
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
3 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
4 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
5 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
6 17.8 6 168. 123 3.92 3.44 18.9 1 0 4 4
7 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6
[[3]]
# A tibble: 14 × 11
mpg cyl disp hp drat wt qsec vs am gear carb
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
2 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
3 16.4 8 276. 180 3.07 4.07 17.4 0 0 3 3
4 17.3 8 276. 180 3.07 3.73 17.6 0 0 3 3
5 15.2 8 276. 180 3.07 3.78 18 0 0 3 3
6 10.4 8 472 205 2.93 5.25 18.0 0 0 3 4
7 10.4 8 460 215 3 5.42 17.8 0 0 3 4
8 14.7 8 440 230 3.23 5.34 17.4 0 0 3 4
9 15.5 8 318 150 2.76 3.52 16.9 0 0 3 2
10 15.2 8 304 150 3.15 3.44 17.3 0 0 3 2
11 13.3 8 350 245 3.73 3.84 15.4 0 0 3 4
12 19.2 8 400 175 3.08 3.84 17.0 0 0 3 2
13 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4
14 15 8 301 335 3.54 3.57 14.6 0 1 5 8
Fit a linear regression model to each subset of the data.
[[1]]
Call:
lm(formula = mpg ~ wt, data = .x)
Coefficients:
(Intercept) wt
39.571 -5.647
[[2]]
Call:
lm(formula = mpg ~ wt, data = .x)
Coefficients:
(Intercept) wt
28.41 -2.78
[[3]]
Call:
lm(formula = mpg ~ wt, data = .x)
Coefficients:
(Intercept) wt
23.868 -2.192
Predict vehicle MPG from observed vehicle weight.
predict() function needs two inputs.[[1]]
1 2 3 4 5 6 7 8
26.47010 21.55719 21.78307 27.14774 30.45125 29.20890 25.65128 28.64420
9 10 11
27.48656 31.02725 23.87247
[[2]]
1 2 3 4 5 6 7
21.12497 20.41604 19.47080 18.78968 18.84528 18.84528 20.70795
[[3]]
1 2 3 4 5 6 7 8
16.32604 16.04103 14.94481 15.69024 15.58061 12.35773 11.97625 12.14945
9 10 11 12 13 14
16.15065 16.33700 15.44907 15.43811 16.91800 16.04103
map() + nest() in tibbles!Using map with a tibble’s is where a lot of the magic can happen.
# A tibble: 3 × 4
cyl cyl_data mod pred_mpg
<dbl> <list> <list> <list>
1 6 <tibble [7 × 10]> <lm> <dbl [7]>
2 4 <tibble [11 × 10]> <lm> <dbl [11]>
3 8 <tibble [14 × 10]> <lm> <dbl [14]>
# A tibble: 32 × 4
cyl wt mpg pred_mpg
<dbl> <dbl> <dbl> <dbl>
1 6 2.62 21 21.1
2 6 2.88 21 20.4
3 6 3.22 21.4 19.5
4 6 3.46 18.1 18.8
5 6 3.44 19.2 18.8
6 6 3.44 17.8 18.8
7 6 2.77 19.7 20.7
8 4 2.32 22.8 26.5
9 4 3.19 24.4 21.6
10 4 3.15 22.8 21.8
# ℹ 22 more rows
How can we print out Timothee Chalamet’s statistics song?
[1] "Statistics, yup" "Statistics, yup" "Statistics, yup" "Statistics, yup"
[5] "Statistics, yup" "Statistics, yup" "Statistics, yup" "Statistics, yup"
[9] "Statistics, yup" "Statistics, yup"
Let’s turn this into a function!
The str_glue() function embeds R expressions in curly brackets that are then evaluated and inserted into the argument string.
The str_flatten() function combines a character vector into a single string.
99 bottles of beer on the wall, 99 bottles of beer. Take one down, pass it around, 98 bottles of beer on the wall…
3 bottles of beer on the wall, 3 bottles of beer
Take one down, pass it around, 2 bottles of beer on the wall
3 bottles of beer on the wall, 3 bottles of beer
Take one down, pass it around, 2 bottles of beer on the wall
2 bottles of beer on the wall, 2 bottles of beer
Take one down, pass it around, 1 bottles of beer on the wall
1 bottles of beer on the wall, 1 bottles of beer
Take one down, pass it around, 0 bottles of beer on the wall
0 bottles of beer on the wall, 0 bottles of beer
Take one down, pass it around, -1 bottles of beer on the wall
No more bottles of beer on the wall, no more bottles of beer. Go to the store, buy some more, 99 bottles of beer on the wall…
bottles_lyrics <- function(n){
if(n == 0){
lyrics <- str_glue("No more bottles of beer on the wall, no more bottles of beer. \nGo to the store, buy some more, 99 bottles of beer on the wall...")
} else{
lyrics <- str_glue("{n} bottles of beer on the wall, {n} bottles of beer \nTake one down, pass it around, {n -1} bottles of beer on the wall")
}
return(lyrics)
}4 bottles of beer on the wall, 4 bottles of beer
Take one down, pass it around, 3 bottles of beer on the wall
3 bottles of beer on the wall, 3 bottles of beer
Take one down, pass it around, 2 bottles of beer on the wall
2 bottles of beer on the wall, 2 bottles of beer
Take one down, pass it around, 1 bottles of beer on the wall
1 bottles of beer on the wall, 1 bottles of beer
Take one down, pass it around, 0 bottles of beer on the wall
No more bottles of beer on the wall, no more bottles of beer.
Go to the store, buy some more, 99 bottles of beer on the wall...
sing_day() function.Tip
str_glue() and str_flatten() might be useful – find their arguments in the documentation.sing_day() function over all days.